def test_config():
sgd_opt = tf.keras.optimizers.SGD(lr=2.0,
nesterov=True,
momentum=0.3,
decay=0.1)
opt = MovingAverage(
sgd_opt,
average_decay=0.5,
num_updates=None,
start_step=5,
dynamic_decay=True,
)
config = opt.get_config()
assert config["average_decay"] == 0.5
assert config["num_updates"] is None
assert config["start_step"] == 5
assert config["dynamic_decay"] is True
new_opt = MovingAverage.from_config(config)
old_sgd_config = opt._optimizer.get_config()
new_sgd_config = new_opt._optimizer.get_config()
for k1, k2 in zip(old_sgd_config, new_sgd_config):
assert old_sgd_config[k1] == new_sgd_config[k2]
def test_config(self):
sgd_opt = tf.keras.optimizers.SGD(
lr=2.0, nesterov=True, momentum=0.3, decay=0.1)
opt = MovingAverage(
sgd_opt,
average_decay=0.5,
num_updates=None,
sequential_update=False)
config = opt.get_config()
self.assertEqual(config['average_decay'], 0.5)
self.assertEqual(config['num_updates'], None)
self.assertEqual(config['sequential_update'], False)
new_opt = MovingAverage.from_config(config)
old_sgd_config = opt._optimizer.get_config()
new_sgd_config = new_opt._optimizer.get_config()
for k1, k2 in zip(old_sgd_config, new_sgd_config):
self.assertEqual(old_sgd_config[k1], new_sgd_config[k2])